The present invention concerns the visualization of images captured in different spectral ranges, e.g., thermography images. Sensors for such imaging are commonly used in a wide range of applications such as: (1) night vision—pedestrian/animal detection in the automotive sector; (2) video surveillance; and (3) driver assistance systems using IR-based systems to handle the influence of external light sources, e.g., the sun. Other applications will be apparent to the skilled person.
It is often desired to display images captured with one dynamic range (e.g. HDR) on displays having a different (usually lower) dynamic range. A method for noise removal and image detail enhancement is typically needed that accounts for the limitations on display devices so as to effectively display HDR, e.g. infrared (IR) images. In this example, in order to represent real world scenes, IR images used to be represented by a HDR that generally exceeds the working range of common display devices (8 bits). Therefore, an effective HDR mapping without losing the perceptibility of details is needed.
There have been presented a vast number of image processing techniques to increase the image contrast, enhance image details and/or reduce the amount of noise. Although most of these techniques are intended for 8 bit still images, they can be adapted and/or combined to address the enhancement of IR image details whilst mapping its HDR into a proper range for display. Indeed, variations of gamma correction (GC) and histogram equalization (HE) approaches have been widely used to fit the raw data into an 8 bit data representation with an increase of the global contrast.
Recent IR image visualization approaches are strongly related to the enhancement of the details in the resulting 8 bit image representation. Glush et al. (S. W. Glushko and C. Salvaggio, “Quantitative analysis of infrared contrast enhancement algorithms,” in Infrared Imaging Systems: Design, Analysis, Modeling, and Testing, 2007, pp. 65 430S: 1-12) limited the noise amplification by the so-called unsharp masking, a technique that defines a detail map to classify noise and detail pixels depending on the intensity of the detail. As suggested by Durand and Dorsey (F. Durand and J. Dorsey, “Fast Bilateral Filtering for the Display of High-Dynamic-Range Images,” ACM Trans. Graph., vol. 21, no. 3, pp. 257-266, July 2002), bilateral filter (BF) has been widely used to define a detail map. Zuo et al. (C. Zuo, Q. Chen, N. Liu, J. Ren, and X. Sui, “Display and detail enhancement for high-dynamic-range infrared images,” Optical Engineering, vol. 50, no. 12, pp. 127 401:1-9, 2011) addressed the gradient reversal artefact on their BF-based Digital Detail Enhancement (BF&DDE) filter by applying a Gaussian low pass filter on the resulting base image component. More recently, He et al. (K. He, J. Sun, and X. Tang, “Guided Image Filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, June 2013) presented a new edge preserving filter, the so called guided filter (GF). As suggested by the authors, the GF might be also considered to define the detail map, instead of using the BF. Indeed, in contrast to the BF, GF does not suffer from the edge artefact and its processing time is significantly smaller.
A problem with known methods is how to achieve mapping of the HDR whilst preserving important image details for data representation in the 8 bits domain.
A further problem is how to identify/classify pixels representing image details and/or pixels that might present noise.
A further problem with known methods is how to achieve noise removal without attenuating or removing image details.
A further problem is how to avoid noise magnification in the detail enhancement process.
A further problem with known methods is how to enhance the global contrast of the IR image.
A further problem is how to avoid global brightness fluctuations from frame to frame (i.e., time consistency).
It is an object of the present invention to provide a system and method that address at least one of the foregoing problems and provide improved techniques for real-time noise removal and image enhancement of high-dynamic range (HDR) images.